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The impressive advances in robotic spatial reasoning over the past decade have relied primarily on rich sensory data provided by laser range finders. Relative to cameras, however, lasers are heavy, bulky, power-hungry, and expensive. This work proposes and evaluates an image-segmentation pipeline that produces range scans from ordinary webcameras. Starting with a nearest-neighbor classification of...
This work presents and evaluates the PixelLaser system, designed to estimate range-to-obstacle scans from single images. Its visual pipeline uses nearest-neighbor texture-matching to segment groundplane (traversable) texture from non-groundplane (obstacle) texture. Using the known pose of a webcamera, the system then transforms these segmentations into range-scans. This paper presents a thorough evaluation...
This work seeks to augment commodity robots to enable service tasks that require global spatial reasoning, e.g., delivery, surveillance, and map maintenance. In addition. We seek to do this as accessibly and inexpensively as possible. To that end we present a design and three prototype platforms that illustrate educational, service-level, and research applications atop the iRobot Create. The prototypes...
This paper presents the iRobot corporation's Roomba vacuum as a low-cost resource for robotics research and education. Sensor and actuation models for unmodified Roombas are presented in the context of both special- and general-purpose spatial-reasoning algorithms, including Monte Carlo Localization and FastSLAM. Further tests probe the feasibility of sensor extensions to the platform. Results demonstrate...
This article studies the following question: “When is it possible to decide, on the basis of images of point-features observed by an imprecisely modeled two-camera stereo vision system, whether or not a prescribed robot positioning task has been precisely accomplished?” Results are shown for three camera model classes: injective cameras, weakly calibrated projective cameras, and uncalibrated projective...
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